Relevant bibliographies by topics / Phenology

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Phenology'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2025

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Journal articles on the topic "Phenology"

1

Macphie, Kirsty H., and Albert B. Phillimore. "Phenology." Current Biology 34, no. 5 (March 2024): R183—R188. http://dx.doi.org/10.1016/j.cub.2024.01.007.

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Colin Irvine. "Cognitive Phenology:." Interdisciplinary Literary Studies 16, no. 1 (2014): 160. http://dx.doi.org/10.5325/intelitestud.16.1.0160.

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Ma, Xin Ping, Hong Ying Bai, Ying Na He, and Shu Heng Li. "The Vegetation Remote Sensing Phenology of Qinling Mountains Based on the NDVI and the Response of Temperature to it." Applied Mechanics and Materials 700 (December 2014): 394–99. http://dx.doi.org/10.4028/www.scientific.net/amm.700.394.

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The acquisition vegetation phenology information by using time series of satellite data is an important aspect of the application of remote sensing and climate change research . Based on the MODOS NDVI time series of images in 2000-2010, Dynamic threshold method and GIS tools were used to extract the vegetation phenology parameters of Qinling Mountains in 2000-2010 , the accuracy of remote sensing phenology results was verified combined with the measured phenological data, And analyzed the characteristis of phenological variation and the relationship between temperature changes and the phenology of Qinling region,and quantified the extent of temperature change on vegetation phenology in a macro scale. Calculated :the trend of vegetation phenology variation based on the NDVI and the results of phenological data are consistent. Results show that NDVI has good revealed effect on vegetation phenology; From 2000 to 2010,it ahead of 1.8 days at the beginning period of vegetation phenology and late back 1.2 days at the end period ; The start phenology NDVI was generally greater than the late phenology on spatial distribution; The effective temperatures and the temperature in spring, growing period had a maximum influence on NDVI at beginning phenology period,the temperatures in summer and autumn had greater impact on the final NDVI .
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Xu, Lingling, Ben Niu, Xianzhou Zhang, and Yongtao He. "Dynamic Threshold of Carbon Phenology in Two Cold Temperate Grasslands in China." Remote Sensing 13, no. 4 (February 5, 2021): 574. http://dx.doi.org/10.3390/rs13040574.

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Plant phenology, especially the timing of the start and the end of the vegetation growing season (SOS and EOS), plays a major role in grassland ecosystem carbon cycles. As the second-largest grassland country in the world, China’s grasslands are mainly distributed in the northern cold temperate climate zone. The accuracies and relations of plant phenology estimations from multialgorithms and data resources are poorly understood. Here, we investigated vegetation phenology in two typical cold temperate grasslands, Haibei (HB) and Inner Mongolia (NM) grasslands, in China from 2001 to 2017. Compared to ground vegetation phenology observations, we analyzed the performance of the moderate resolution imaging spectroradiometer MODIS phenology products (MCD12Q2) and two remote sensing-based vegetation phenology algorithms from the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time series (five satellite-based phenology algorithms). The optimal algorithm was used to compare with eddy covariance (EC)-based carbon phenology, and to calculate the thresholds of carbon phenology periods (SOSt and EOSt) in each site. Results showed that satellite-based phenology estimations (all five algorithms in this study) were strongly coupled with the temporal variation of the observed phenological period but significantly overestimated the SOS, predicting it to be over 21 days later than the field data. The carbon phenology thresholds of HB grassland (HB_SOSt and HB_EOSt) had a significant upward trend, with the multiyear average values being 0.14 and 0.29, respectively. In contrast, the thresholds of NM grasslands (NM_SOSt and NM_EOSt) also showed a certain upward trend, but it was not significant (p > 0.05), with the multiyear average values being 0.17 and 0.2, respectively. Our study suggested the thresholds of carbon phenology periods (SOSt and EOSt, %) could be simply and effectively estimated based on their significant relationship with the EC-based maximum of gross primary productivity observations (GPPmax) at a specific site and time. Therefore, this study suggested the thresholds of carbon phenology were not fixed even in a specific ecosystem, which also provided simple bridges between satellite-based vegetation phenology and EC-based carbon phenology in similar grasslands.
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Wang, Cong, Yijin Wu, Qiong Hu, Jie Hu, Yunping Chen, Shangrong Lin, and Qiaoyun Xie. "Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations." Remote Sensing 14, no. 13 (June 23, 2022): 3018. http://dx.doi.org/10.3390/rs14133018.

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Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives.
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Ding, Haiyong, Luming Xu, Andrew J. Elmore, and Yuli Shi. "Vegetation Phenology Influenced by Rapid Urbanization of The Yangtze Delta Region." Remote Sensing 12, no. 11 (June 1, 2020): 1783. http://dx.doi.org/10.3390/rs12111783.

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Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated. Ecosystem responses to each of these drivers are perhaps most clearly seen in phenology changes due to global climate change (warming climate) and urbanization (heat island effect). The phenology of vegetation can influence many important ecological processes, including primary production, evapotranspiration, and plant fitness. Therefore, evaluating the interacting effects of urbanization and climate change on vegetation phenology has the potential to provide information about the long-term impact of global change. Using remotely sensed time series of vegetation on the Yangtze River Delta in China, this study evaluated the impacts of rapid urbanization and climate change on vegetation phenology along an urban to rural gradient over time. Phenology markers were extracted annually from an 18-year time series by fitting the asymmetric Gaussian function model. Thermal remote sensing acquired at daytime and nighttime was used to explore the relationship between land surface temperature and vegetation phenology. On average, the spring phenology marker was 9.6 days earlier and the autumn marker was 6.63 days later in urban areas compared with rural areas. The spring phenology of urban areas advanced and the autumn phenology delayed over time. Across space and time, warmer spring daytime and nighttime land surface temperatures were related to earlier spring, while autumn daytime and nighttime land surface temperatures were related to later autumn phenology. These results suggest that urbanization, through surface warming, compounds the effect of climate change on vegetation phenology.
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Zhang, Jing, Shouzhi Chen, Zhaofei Wu, and Yongshuo H. Fu. "Review of vegetation phenology trends in China in a changing climate." Progress in Physical Geography: Earth and Environment 46, no. 6 (November 27, 2022): 829–45. http://dx.doi.org/10.1177/03091333221114737.

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Vegetation phenology is sensitive to climate change and has been defined as the footprint of ongoing climate change. Previous studies have shown that the spatial difference in China’s vegetation phenology varies substantially in both spring and autumn. Here, we reviewed phenological dynamics at the national and the regional scale of China over the period 1982−2020 using a remote sensing-based dataset and meta-analysis from phenological studies in China. We also explored the underlying mechanisms of both spring and autumn phenology and discussed potential phenological studies under future climate conditions. We found that, over the past four decades, the spring phenology advanced at a rate of 0.23 ± 0.47 days/year, while the autumn phenology was delayed at a rate of 0.17 ± 0.46 days/year. This led to an extended vegetation growth season of approximately 5 days per decade. The trends in the spring and autumn phenology were spatially specific in the Northern region, Northwest region, Qinghai–Tibet region, and Southern region: the change in spring phenology was −0.16, −0.46, −0.18, and −0.13 days/year, respectively, while the change in autumn phenology was 0.02, 0.32, 0.09, and 0.28 days/year, respectively. We also explored the dominant climatic drivers of regional phenological changes. We found that temperature was the dominant factor for spring phenology in cold regions, while precipitation, radiation, and temperature co-determined spring phenology in warm regions. The autumn phenology was affected by all three environmental cues but the effect of temperature was larger than that of radiation and precipitation across all regions. In future climate warming conditions, we recommend that studies focus on the phenological feedback mechanisms, such as the climatic and hydrological effects of vegetation changes, and agricultural phenology to investigate its fundamental role in crop productivity, especially under extreme climate events, to ensure national food security and ecological security.
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Wang, Xinwei, Jianhao Li, Jianghua Zheng, Liang Liu, Xiaojing Yu, Ruikang Tian, and Mengxiang Xing. "How Do Changes in Grassland Phenology and Its Responses to Extreme Climatic Events in Central Asia?" Land 14, no. 1 (January 14, 2025): 160. https://doi.org/10.3390/land14010160.

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Extreme climate events have become more frequent under global warming, significantly affecting vegetation phenology and carbon cycles in Central Asia. However, the mediating effects of intensity of compound drought and heat events (CDHEs) and compound moisture and heat events (CMHEs) on grassland phenology and their trends in the relative contributions to grassland phenology over time have remained unclear. Based on the calculation results of grassland phenology and compound events (CEs), this study used trend analysis, partial least squares regression structural equation modeling (PLS-SEM), and ridge regression analysis to investigate the mediating effect and the temporal trend in relative contribution of CEs to grassland phenology in Central Asia, and the magnitude of sensitivity of grassland phenology to CEs. This study revealed that the start of season (SOS) was advanced by 0.4 d·a−1, end of season (EOS) was delayed by 0.5 d·a−1, and length of season (LOS) extended by 0.8 d·a−1 in 1982–2022. The duration of the CDHEs (0−37 days) was greater than that of the CMHEs (0−9 days) in Central Asia. The direct effects of CDHEs and CMHEs on grassland phenology were generally negative, except for the direct positive effect of CDHEs on LOS. The indirect effects of temperature and precipitation on grassland phenology through CDHEs and CMHEs were greater than their direct effects on phenology. The relative contribution of CDHEs to grassland phenology was consistently greater than that of CMHEs, and both the relative contribution curves showed a significant upward trend. The sensitivity of grassland phenology to CDHEs was higher than its sensitivity to CMHEs at 0.79 (SOS), 1.18 (EOS), and 0.72 (LOS). Our results emphasize the mediating effects of CDHEs and CMHEs on grassland phenology. Under the influence of CDHEs and CMHEs, the LOS will further lengthen in the future.
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Meier, Michael, and Christof Bigler. "Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections." Geoscientific Model Development 16, no. 23 (December 11, 2023): 7171–201. http://dx.doi.org/10.5194/gmd-16-7171-2023.

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Abstract. Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. The length of the growing season is affected by climate change because autumn phenology responds to climatic conditions. Thus, the timing of autumn phenology is often modeled to assess possible climate change effects on future CO2-mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 process-oriented models of autumn leaf phenology, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 central European sites each. Phenology models had the largest influence on model performance. The best-performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology; (2) calibrated with the generalized simulated annealing algorithm; and (3) based on systematically balanced or stratified samples. Autumn phenology was projected to shift between −13 and +20 d by 2080–2099 compared to 1980–1999. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had an influence 8 to 22 times greater than the phenology models. Warmer climate scenarios and better-performing models predominantly projected larger backward shifts than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes, and we advocate for species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that current models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.
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Harper, Geoffrey. "Lessons from Phenology." Sibbaldia: the International Journal of Botanic Garden Horticulture, no. 8 (October 31, 2010): 149–64. http://dx.doi.org/10.24823/sibbaldia.2010.143.

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Twenty provisional multiple-regression models based on a small data set are presented to account for the timing of first-flower date and other phenological events. Biological mechanisms are suggested to explain the pattern of temperature-dependent developmental stages. The implications for how plants and vegetation are likely to react to climate change are discussed, and attention is drawn to the importance of within-taxon variation in phenological behaviour.
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Dissertations / Theses on the topic "Phenology"

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Palm, Anna. "Flight phenology of oligolectic solitary bees are affected by flowering phenology." Thesis, Linköpings universitet, Biologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177651.

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Understanding the relationships between solitary bees’ flight phenology and flowering phenology is important in the context of global warming. Using Swedish citizen science data, observations of oligolectic solitary bees and flowering phenology were used together with temperature data. All five bees studied had flight period that overlapped with the flowering period their corresponding host plant. None of the species were affected by the temperature, although there was a correlation between earliest observations of flowering phenology and flight phenology. The later the flowering observation was made, the later the flight observation was made. No correlation was found between the length of flight period and length of the flowering period. Increasing temperature is not the only factor that effects flight phenology and flowering phenology.
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Warren, Peter L., and LoriAnne Barnett. "Phenology: Using Phenology as a Tool for Education, Research, and Understanding Environmental Change." College of Agriculture, University of Arizona (Tucson, AZ), 2014. http://hdl.handle.net/10150/324032.

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Phenology is defined and described in terms of how we use observations in education and research. Suggestions for implementing phenology lessons using examples from 4-H youth development and Master Gardener and citizen science training.
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Jarvis, Claire H. "Insect phenology : a geographical perspective." Thesis, University of Edinburgh, 1999. http://hdl.handle.net/1842/22349.

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The rate of insect development (phenology) is strongly associated with temperature. Within the biological literature, phenologies are estimated largely on the basis of sparsely located point meterological data. The significance of incorporating a geographical dimension was explored in two application areas where phenologies are used, pest risk assessment (PRA) and integrated pest management (IPM). Colorado beetle (leptinotarsa decemlineata) and codling moth (Cydia pomonella) were used as representative non-indigenous and indigenous test organisms. To ensure relevance to both pest risk assessment and integrated pest management applications, phenology models were run using daily meterological data throughout England and Wales. Interpolation was chosen as an efficient means to create spatial temperature 'surfaces' from distributed daily maximum and minimum temperature data observed at a subset of 174 meteorological stations. Because insect pests are known to be highly sensitive to temperature, considerable attention was paid to minimising the errors generated as part of this process relative to that in previous applied agricultural studies. Comparisons between the commonly used trend surface and inverse distance weighting methods of interpolation were made with partial thin plate splines and ordinary kriging. Unlike earlier work, automatic parameter selection was used to calibrate all the interpolation techniques and care was taken to ensure the comparability of estimated temperature values. Error in estimates by all methods was reduced using a number of guiding topo-climate and land cover covariates. The most favourable estimates of maximum and minimum temperatures throughout the country and over the annual cycle were partial thin plate splines, with daily average r.m.s. accuracies computed using jack-knife cross-validation of 0.8°C and 1.13°C respectively. Partial thin plate splines were also found to be more computationally efficient than both inverse distance weighting and de-trended ordinary kriging. This use of jack-knife cross-validation was assessed using a fully independent data set of a further 100 data points, and was found to be statistically comparable. Providing the interaction between phenology models and sequences of geographically relevant temperature data at this daily step and national coverage necessitated the construction of tailor made research software for the project. The coupled temperature interpolation/phenology modelling system was used to provide a range of outputs to explore the accuracy of predicted phenologies over space and time.
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Clements, Michelle N. "Phenology in a wild mammal population." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/14599.

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Sparks, Timothy Hugh. "The influence of climate warming on phenology." Thesis, Sheffield Hallam University, 2001. http://shura.shu.ac.uk/23517/.

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Phenology, the study of timings of natural events, is the longest written biological record in the UK. It has thus proved invaluable in revealing how species have responded to recent climate warming. I have played a major role in achieving Scientific 'legitimacy' for the subject and there is a growing urgency to demonstrate climate induced effects to both a scientific and a general audience. My phenological publications fall into four broad areas. 1 . Utilising historic data. Many historic data sets have languished in obscurity for >50 years. Identification and examination of some of these data has revealed how biological events responded to past fluctuations in temperature. The typical response of c.6 days earlier for each 1°C warming has enabled a prediction of response to future climate. National data sets have given greater confidence in these results. 2. Bird phenology. Bird data, particularly that on migration timing, forms a huge resource of phenological material. I have examined the role of temperature in bird phenology and on migration patterns from various sources of data and have begun to extend these studies through international collaboration (two further papers 'in press'). In general, the response of birds is more variable and not as great as that of plants and invertebrates. 3. Other taxa. Post-war changes have already taken place in the timing of a wide range of taxa. In some instances events are at least three weeks earlier. These results have encouraged me to resurrect a phenology network after a 50-year break (www.phenology.org.uk). 4. Increasing awareness. Changes in phenology are readily understood by various sectors of the public and are a good vehicle with which to demonstrate climate change. The UK Government has now accepted phenological events as Climate Change Indicators.
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Black, Caitlin Emily. "Variation in the phenology of Pygoscelis penguins." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:00c306b4-f7c4-4f11-8749-1e3ae118746b.

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Variation in phenology is linked to the timing of environmental variables and influences survival at both the individual and colony level. Therefore, understanding a species' annual cycle is vital to its ecology and conservation. By reviewing literature on Pygoscelis penguin phenology in Chapter 1, I identify major gaps, both spatially and temporally, in our knowledge of the timing of events in the three species (Adeélie, Pygoscelis adeliae; chinstrap, and Pygoscelis antarctica; gentoo, Pygoscelis papua): particularly, 1) during the guard phase, 2) their behaviour in winter, 3) the phenology of colonies inhabiting locations away from scientific bases, and 4) the general phenology of chinstrap penguins. Chapter 2 assesses which time-lapse camera methods are most relevant to seabird research, highlighting the capabilities and limitations of cameras in past studies and how they may be best applied to future research. Chapter 3 examines the timing of the guard phase in gentoo penguins and how chick aggregation behaviours vary across several sites. Chapters 4 and 5 show variation in winter abundance at breeding sites in both gentoo and Adélie penguins related to abiotic factors and colony location. Lastly, Chapter 6 fills in gaps in the known timing and duration of phenology events in gentoo and chinstrap penguins across their full latitudinal ranges, while relating these timings to chick survival. In the conclusion, I summarize the main findings of the thesis, focusing on three major themes that were observed across the four data chapters and their implications: 1) behaviours are not consistent across colony locations 2) nor between years, and these behaviours depend on 3) local environmental conditions. I then synthesize these empirical findings from each of these chapters, discuss the implication of these findings to ecological theory and conservation policy, highlight some of the limitations of these studies, and recommend possibilities for future research.
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Aasa, Anto. "Changes in phenological time series in Estonia and central and eastern Europe 1951-1998 : relationships with air temperature and atmospheric circulation /." Tartu, Estonia : Tartu University Press, 2005. http://dspace.utlib.ee/dspace/bitstream/10062/847/5/aasa.pdf.

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Zhou, Qiang. "Disaggregating tree and grass phenology in tropical savannas." Thesis, The University of North Dakota, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3724867.

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Savannas are mixed tree-grass systems and as one of the world's largest biomes represent an important component of the Earth system affecting water and energy balances, carbon sequestration and biodiversity as well as supporting large human populations. Savanna vegetation structure and its distribution, however, may change because of major anthropogenic disturbances from climate change, wildfire, agriculture, and livestock production. The overstory and understory may have different water use strategies, different nutrient requirements and have different responses to fire and climate variation. The accurate measurement of the spatial distribution and structure of the overstory and understory are essential for understanding the savanna ecosystem.

This project developed a workflow for separating the dynamics of the overstory and understory fractional cover in savannas at the continental scale (Australia, South America, and Africa). Previous studies have successfully separated the phenology of Australian savanna vegetation into persistent and seasonal greenness using time series decomposition, and into fractions of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil (BS) using linear unmixing. This study combined these methods to separate the understory and overstory signal in both the green and senescent phenological stages using remotely sensed imagery from the MODIS (MODerate resolution Imaging Spectroradiometer) sensor. The methods and parameters were adjusted based on the vegetation variation.

The workflow was first tested at the Australian site. Here the PV estimates for overstory and understory showed best performance, however NPV estimates exhibited spatial variation in validation relationships. At the South American site (Cerrado), an additional method based on frequency unmixing was developed to separate green vegetation components with similar phenology. When the decomposition and frequency methods were compared, the frequency method was better for extracting the green tree phenology, but the original decomposition method was better for retrieval of understory grass phenology. Both methods, however, were less accurate than in the Cerrado than in Australia due to intermingling and intergrading of grass and small woody components.

Since African savanna trees are predominantly deciduous, the frequency method was combined with the linear unmixing of fractional cover to attempt to separate the relatively similar phenology of deciduous trees and seasonal grasses. The results for Africa revealed limitations associated with both methods. There was spatial and seasonal variation in the spectral indices used to unmix fractional cover resulting in poor validation for NPV in particular. The frequency analysis revealed significant phase variation indicative of different phenology, but these could not be clearly ascribed to separate grass and tree components.

Overall findings indicate that site-specific variation and vegetation structure and composition, along with MODIS pixel resolution, and the simple vegetation index approach used was not robust across the different savanna biomes. The approach showed generally better performance for estimating PV fraction, and separating green phenology, but there were major inconsistencies, errors and biases in estimation of NPV and BS outside of the Australian savanna environment.

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Kyereh, Boateng. "Seed phenology and germination of Ghanaian forest trees." Thesis, University of Aberdeen, 1994. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU068828.

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Seed production and germination of some timber tree species were studied in Ghana for possible prediction of seed yield and natural regeneration. Seed phenology in 13 species was monitored for two years, using permanent seed traps in two forest sites. Seed germination tests were conducted in neutral, green shade and dark in shade houses for 20 species. In the forest, germination was tested in forest gaps receiving different irradiances. Fruiting frequency ranged from twice in each year to supra-annual fruiting. Fruiting periods for species were consistent between years. Fruiting synchrony was higher among individuals of a population than between sites for the same species. Fecundity differed between years for the majority of species and between sites for species common to both sites. Premature fruit abscission was quite common. Maximum seed weight and percentage germination occurred during peak fall of mature seeds. Seeds of the majority of species germinated equally in light and dark and also in neutral and low red: far red ratio. These included some species previously classified as pioneers. In the forest germination was depressed in a large clearing for the majority of species. The use of photoblastic germination alone to define pioneers leads to a smaller group of pioneer species than is presently recognised. Large gaps due to logging may discourage natural regeneration.
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Sevenello, Montagner Jose Manuel. "Temporal Synchrony between Ground-Nesting Bees and Spring Ephemerals in an Eastern Hardwood Forest Ecosystem." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38299.

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Changes in phenology due to climate warming could disrupt temporal overlap between interacting organisms when previously synchronized species respond to climate change at different rates. Phenologies of plants and insects are known to be sensitive to temperature and/or timing of snowmelt, with warmer temperatures and earlier snowmelt generally advancing spring flowering and emergence; however, some groups of pollinators, such as solitary bees, have been little explored in this context. One striking aspect of eastern hardwood forests is the emergence of understory wildflowers each spring, most of which rely, at least to some extent, on wild native pollinators for seed set. Without an understanding of the environmental drivers of phenology of these species, we have little ability to predict whether pollinators will continue to be well synchronized with flowering as the climate changes. In this study, I determined how spring temperatures and timing of snowmelt influence the phenology of spring wildflowers, activity of bees, and their temporal overlap in Gatineau Park, Québec. From 2013 to 2018, I characterized bee activity phenology and flowering phenology of understory plants in multiple study plots, focusing on early-flowering Anemone spp. and later-flowering Trillium grandiflorum. The sampled bee community was dominated by Andrena, Lasioglossum, and Nomada, all of which have similar activity periods. Degree-day accumulation was a better predictor of Anemone and Nomada phenology than were day of year or snowmelt date, whereas T. grandiflorum appeared to be more sensitive to photoperiodic cues; since day of year was the variable that best described its phenology. Activity periods of Andrena and Lasioglossum were equally well described by degree-day accumulation and by day of year. No taxon’s phenology was best predicted by snowmelt date. Despite these differences among taxa in the identities of the best predictors of phenology, bee activity and plant flowering phenologies responded at similar rates to interannual and among-site variation in snowmelt date and early spring temperature. Temporal overlap between flowering and bee activity was similar over the years of this study and was affected neither by snowmelt date nor by temperature. These results suggest that interacting plant and bee taxa may respond to different environmental variables but still maintain their synchrony under the conditions recorded so far.
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Books on the topic "Phenology"

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T, Hodges, ed. Predicting crop phenology. Boca Raton: CRC Press, 1991.

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Noormets, Asko, ed. Phenology of Ecosystem Processes. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0026-5.

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Ewusie, J. Yanney. Phenology in tropical ecology. Accra: Ghana University Press, 1992.

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4

Green, Jerry E. Phenology: An annotated bibliography. Monticello, Ill., USA: Vance Bibliographies, 1990.

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Chen, Xiaoqiu. Spatiotemporal Processes of Plant Phenology. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-49839-2.

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Schwartz, Mark D., ed. Phenology: An Integrative Environmental Science. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-007-0632-3.

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Schwartz, Mark D., ed. Phenology: An Integrative Environmental Science. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6925-0.

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Donald, Schwartz Mark, ed. Phenology: An integrative environmental science. Dordrecht: Kluwer Academic Publishers, 2003.

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Schwartz, Mark Donald. Phenology: An integrative environmental science. Dordrecht: New York, 2013.

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Schwartz, Mark D., ed. Phenology: An Integrative Environmental Science. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75027-4.

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Book chapters on the topic "Phenology"

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Frank, J. Howard, J. Howard Frank, Michael C. Thomas, Allan A. Yousten, F. William Howard, Robin M. Giblin-davis, John B. Heppner, et al. "Phenology." In Encyclopedia of Entomology, 2834. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6359-6_2897.

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Overdieck, Dieter. "Phenology." In CO2, Temperature, and Trees, 175–82. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1860-2_11.

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Shivanna, K. R., and Rajesh Tandon. "Phenology." In Reproductive Ecology of Flowering Plants: A Manual, 19–23. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2003-9_3.

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Tomasi, Diego, Federica Gaiotti, and Gregory V. Jones. "Phenology." In The Power of the Terroir: the Case Study of Prosecco Wine, 55–64. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0628-2_5.

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Jones, Gregory V. "Winegrape Phenology." In Phenology: An Integrative Environmental Science, 523–39. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-007-0632-3_32.

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Kimball, John. "Vegetation Phenology." In Encyclopedia of Remote Sensing, 886–90. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_188.

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Lee, Pei-Hsuan, Yao-Moan Huang, and Wen-Liang Chiou. "Fern Phenology." In Current Advances in Fern Research, 381–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75103-0_18.

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Spano, Donatella, Richard L. Snyder, and Carla Cesaraccio. "Mediterranean Phenology." In Phenology: An Integrative Environmental Science, 173–96. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6925-0_10.

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Jones, Gregory V. "Winegrape Phenology." In Phenology: An Integrative Environmental Science, 563–84. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6925-0_30.

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Spano, Donatella, Richard L. Snyder, and Carla Cesaraccio. "Mediterranean Phenology." In Phenology: An Integrative Environmental Science, 171–201. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75027-4_9.

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Conference papers on the topic "Phenology"

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Camintia, Leila Lucia, Veraldo Liesenberg, Andre Felipe Hess, Fabio Henrique De Barros, Marcos Benedito Schimalski, Fabio Marcelo Breunig, and Leonardo Josoe Biffi. "Corn phenology and productivity with PlanetScope images." In 2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/agro-geoinformatics262780.2024.10660696.

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Wei, Shanshan, Kim Hwa Lim, Ken Yoong Lee, Li Ming Tan, Boon Jin Chew, and Soo Chin Liew. "Cotton Phenology Detection Using Sentinel-1 Time Series Data." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 1260–63. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640465.

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Deng, Qinwu, Yaodong Yang, and Maolin Zhang. "Methods for extracting vegetation phenology information and results analysis." In Second International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), edited by Bin Zou and Yaoping Cui, 32. SPIE, 2024. http://dx.doi.org/10.1117/12.3048668.

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Cirone, Richard, Martha Anderson, Jisung Chang, Haoteng Zhao, Feng Gao, and Christopher Hain. "Retiming Evaporative Stress Index to Vegetation Phenology in Iowa Croplands." In 2024 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/agro-geoinformatics262780.2024.10660771.

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Shao, Q., C. Huang, and J. F. Huang. "FOREST PHENOLOGICAL TRENDS IN THE MIDDLE AND HIGH LATITUDE OF THE NORTHERN HEMISPHERE." In Лесные экосистемы в условиях изменения климата: биологическая продуктивность и дистанционный мониторинг. Crossref, 2020. http://dx.doi.org/10.25686/7233.2020.6.58831.

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Vegetation phenology is the study of periodically recurring patterns of growth and development of plants, which affect terrestrial ecosystem carbon, energy budget balance, fire disturbance, and climate– biosphere interactions. The increases in surface temperature had already altered the extent of vegetation phenology. Vegetation phenology can make some responses to climate factors, and the current climate change has attracted more research for the trend of vegetation phenology and its causes. The purpose of this paper is to investigate the spatial and temporal trend of forest phenology at mid and high latitude in the Northern Hemisphere (50°N-90°N, 180°W-180°E) over the period 2001–2017 using Collection 6 MODIS Land Cover Dynamics (MCD12Q2) datasets. The results indicated that SOS has a significant advanced trend, EOS has a significant delayed trend and LOS showed a significant extended trend on the whole. The significant advancement of SOS and extension of LOS mainly occurred in central Russia, the north and southwest of North America. Meanwhile, EOS showed a delayed trend in the south of Russia, the north and southwest of Canada and Alaska.
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Yalcin, Hulya. "Phenology recognition using deep learning." In 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT). IEEE, 2018. http://dx.doi.org/10.1109/ebbt.2018.8391423.

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Bradley, Andrew, France Gerard, Nicolas Barbier, Graham Weedon, Chris Huntingford, Przemyslaw Zelazowski, Liana Anderson, Luiz Eduardo O. C. de Aragao, and Jorg Kaduk. "Template phenology for vegetation models." In 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009). IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5417570.

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de Beurs, K., and G. Henebry. "War, Drought, and Phenology: Changes in the Land Surface Phenology of Afghanistan Since 1982." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.630.

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Roerink, G. J., M. H. G. I. Danes, O. Gomez Prieto, A. J. W. de Wit, and A. J. H. van Vliet. "Deriving plant phenology from remote sensing." In 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp). IEEE, 2011. http://dx.doi.org/10.1109/multi-temp.2011.6005098.

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Yalcin, Hulya. "Phenology recognition using deep learning: DeepPheno." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404165.

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Reports on the topic "Phenology"

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Wright, Kirsten. Collecting Plant Phenology Data In Imperiled Oregon White Oak Ecosystems: Analysis and Recommendations for Metro. Portland State University, March 2020. http://dx.doi.org/10.15760/mem.64.

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Highly imperiled Oregon white oak ecosystems are a regional conservation priority of numerous organizations, including Oregon Metro, a regional government serving over one million people in the Portland area. Previously dominant systems in the Pacific Northwest, upland prairie and oak woodlands are now experiencing significant threat, with only 2% remaining in the Willamette Valley in small fragments (Hulse et al. 2002). These fragments are of high conservation value because of the rich biodiversity they support, including rare and endemic species, such as Delphinium leucophaeum (Oregon Department of Agriculture, 2020). Since 2010, Metro scientists and volunteers have collected phenology data on approximately 140 species of forbs and graminoids in regional oak prairie and woodlands. Phenology is the study of life-stage events in plants and animals, such as budbreak and senescence in flowering plants, and widely acknowledged as a sensitive indicator of environmental change (Parmesan 2007). Indeed, shifts in plant phenology have been observed over the last few decades as a result of climate change (Parmesan 2006). In oak systems, these changes have profound implications for plant community composition and diversity, as well as trophic interactions and general ecosystem function (Willis 2008). While the original intent of Metro’s phenology data-collection was to track long-term phenology trends, limitations in data collection methods have made such analysis difficult. Rather, these data are currently used to inform seasonal management decisions on Metro properties, such as when to collect seed for propagation and when to spray herbicide to control invasive species. Metro is now interested in fine-tuning their data-collection methods to better capture long-term phenology trends to guide future conservation strategies. Addressing the regional and global conservation issues of our time will require unprecedented collaboration. Phenology data collected on Metro properties is not only an important asset for Metro’s conservation plan, but holds potential to support broader research on a larger scale. As a leader in urban conservation, Metro is poised to make a meaningful scientific contribution by sharing phenology data with regional and national organizations. Data-sharing will benefit the common goal of conservation and create avenues for collaboration with other scientists and conservation practitioners (Rosemartin 2013). In order to support Metro’s ongoing conservation efforts in Oregon white oak systems, I have implemented a three-part master’s project. Part one of the project examines Metro’s previously collected phenology data, providing descriptive statistics and assessing the strengths and weaknesses of the methods by which the data were collected. Part two makes recommendations for improving future phenology data-collection methods, and includes recommendations for datasharing with regional and national organizations. Part three is a collection of scientific vouchers documenting key plant species in varying phases of phenology for Metro’s teaching herbarium. The purpose of these vouchers is to provide a visual tool for Metro staff and volunteers who rely on plant identification to carry out aspects of their job in plant conservation. Each component of this project addresses specific aspects of Metro’s conservation program, from day-to-day management concerns to long-term scientific inquiry.
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Sheehan, Katharine A. User's guide for GMPHENL: a gypsy moth phenology model. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experimental Station, 1992. http://dx.doi.org/10.2737/ne-gtr-158.

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Sheehan, Katharine A. User's guide for GMPHENL: a gypsy moth phenology model. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experimental Station, 1992. http://dx.doi.org/10.2737/ne-gtr-158.

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Murray, Georgia. Appalachian Mountain Club Final Report to the Forest Ecosystem Monitoring Collaborative Project: Monitoring plant phenology & climate metrics in Northeast mountains; filling gaps in complex landscapes. Forest Ecosystem Monitoring Cooperative, July 2024. http://dx.doi.org/10.18125/b667sj.

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Wirtz, William O. Avifauna in southern California chaparral: seasonal distribution, habitat association, reproductive phenology. Berkeley, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 1991. http://dx.doi.org/10.2737/psw-rp-209.

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Schrader-Patton, Charlie, Nancy E. Grulke, and Jacqueline Ott. Monitoring land surface phenology in near real time by using PhenoMap. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2020. http://dx.doi.org/10.2737/pnw-gtr-982.

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Schrader-Patton, Charlie, Nancy E. Grulke, and Jacqueline Ott. Monitoring land surface phenology in near real time by using PhenoMap. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2020. http://dx.doi.org/10.2737/pnw-gtr-982.

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Thoma, David, Jolie Gareis, David Thoma, and Jolie Gareis. Landscape phenology, vegetation condition, and relations with climate at Bryce Canyon National Park: 2000?2019. National Park Service, 2024. http://dx.doi.org/10.36967/2305968.

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Climate determines the vegetation composition in parks and climate change will affect vegetation condition and composition in the future. This study evaluates vegetation sensitivity to weather and climate for the purpose of understanding climate-vegetation relationships that can be used to understand what vegetation types may change. Specifically, it evaluates where change is likely, when change may happen and why change may occur. This information is needed to help avoid surprises and can be used to help plan for inevitable change. Satellite images were analyzed with climate data to quantify vegetation sensitivity to climate and drought tolerance. Historic trends in vegetation production and phenology were evaluated and analysis with climate data identified which aspects of climate were most important to annual production and phenology for different vegetation types. Additionally, trends in climate and climate drivers of vegetation phenology were also identified. Finally, the information generated in this study was placed in the context of a climate adaptation planning framework to demonstrate how it can be used for long-range planning.
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Rykken, Jessica. Tracking plant phenology and pollinator diversity across Alaskan National Parks: A pilot study. National Park Service, August 2021. http://dx.doi.org/10.36967/nrr-2287170.

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Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Colorado National Monument, 2000–2019. National Park Service, May 2022. http://dx.doi.org/10.36967/nrr-2293476.

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